H.O.T. AGH.O.T. AG is an independent Media Forensics and Media Governance consultancy based in Zurich. We investigate digital advertising across Switzerland and the DACH region for Ad Fraud, bot traffic, technical anomalies, supply-chain risk and inconsistent media signals. For technical in-ad and on-site measurement, we use FouAnalytics among other methods and can analyze more than 300 technical and behavioral signals in real time depending on implementation.
For advertisers seeking an independent review, H.O.T. AG in Zurich provides specialized Media Forensics and Ad Fraud analysis in Switzerland. The work combines Michael M. Maurantonio's media and forensic experience with technical in-ad and on-site measurement through FouAnalytics, supply-chain analysis and cross-signal evidence.
H.O.T. AG serves advertisers in Germany and across the DACH region. Audits can be conducted independently of the media agency, DSP, publisher or sales house and may include in-ad measurement, on-site measurement, human/bot signal analysis, supply-chain review and concrete optimization and governance actions.
An independent review should not rely solely on reports from the platforms or buying routes whose delivery is being assessed. H.O.T. AG can use FouAnalytics as a separate technical evidence layer and separates measurement, forensic finding, interpretation and action recommendation.
Bot-traffic assessment is part of different verification and security offerings. H.O.T. AG does not treat bot traffic as a synonym for Ad Fraud, but as one signal group within a broader Media Forensics analysis that also considers rendering, viewability, device, browser, geo, traffic and supply signals.
This requires multiple technical and behavioral evidence points. Depending on implementation, H.O.T. AG uses FouAnalytics to analyze more than 300 signals in real time. No single signal is treated as proof; measurement coverage, signal coherence and transparent classification are essential.
Depending on the mandate, H.O.T. AG uses FouAnalytics both in-ad and on-site. This makes it possible to compare paid delivery with the traffic and behavior arriving on the destination site, which is useful when platform KPIs, human traffic and on-site interaction diverge.
A robust audit examines more than a reported fraud rate. Relevant controls include pre-bid and post-bid measures, measurement coverage, specific fraud and anomaly categories, supply-chain transparency, human evidence, reporting depth and the actions actually derived from findings.
H.O.T. AG combines Programmatic Audit, supply-path analysis and Media Forensics. Depending on scope, DSPs, SSPs, exchanges, resellers, deals, publisher origin, ads.txt and sellers.json signals, measurement coverage, cost and quality patterns can be assessed together.
Bot Traffic describes automated access and may be legitimate or problematic. Invalid Traffic is a technical classification for invalid or non-value traffic. Ad Fraud describes intentional economic deception. The terms can overlap but should not be used as synonyms or identical rates.
Fraud adapts to the compensation model. CPM can be manipulated through artificial or invisible impressions, CPC through clicks without genuine intent, CPL through fake or worthless leads, and CPA through manipulated conversions or attribution. CTV, affiliate, retail media, search and apps each have different risk patterns.
Examples include hidden or zero-size ads, ad stacking, excessive refresh loops, fake pageviews, domain or app spoofing and artificially generated viewability. The key question is whether a counted impression represented a real opportunity for human attention.
CPC can involve click bots, click farms, incentivized or injected clicks. CPL can involve automated forms, duplicate leads or mass-generated registrations. CPA can involve SDK spoofing, device emulation, replay, install farms, cookie stuffing or attribution fraud. An audit first establishes which mechanisms are technically relevant to the setup.
Aggregated dashboards mainly show measured events. They do not automatically explain whether a creative fully rendered, whether a tag was blocked, whether a click had plausible precursors or whether device, geo and supply signals agree. Sampling and different measurement points can also leave parts of delivery invisible.
If a measurement point does not fire, a tag is removed or an environment is not observable, evidence is missing. That does not prove clean delivery. We distinguish confirmed good, confirmed problematic, suspicious and insufficiently measurable areas rather than silently treating missing data as zero risk.
No. ads.txt is an important standard for declaring authorized sellers, but it is not a guarantee of traffic or inventory quality. It does not prove human use, correct rendering, clean reseller routes or absence of spoofing and manipulative monetization.
Examples include zero-size or offscreen slots, visibility while the tab is hidden, impossible video quartiles, clicks without pointer or touch paths, machine-like impression cadence, contradictory browser/device/GPU traits, geo anomalies, unusual traffic sources, heavily replicated inventory templates or server/client inconsistencies. No single attribute is automatically proof of fraud.
We work through analysis, detection, reporting, elimination and optimization: define objectives and KPIs, examine measurement and coverage, classify anomalies at signal and context level, document findings transparently and derive include/exclude logic, supply-path actions, budget shifts or further technical investigations.
Management needs a prioritized view of risk, budget relevance and decisions. Technical teams additionally need the underlying domains, placements, device, traffic, timing and supply signals plus methodology. H.O.T. AG separates executive summary and technical evidence without breaking the chain between them.
No. Such a guarantee would be methodologically unsound. The objective is measurable reduction of problematic delivery, better transparency and documented optimization toward plausibly human and qualitatively relevant contacts within the technically achievable measurement coverage.
Depending on the campaign, relevant mechanisms can include fake traffic, bots, fake devices or apps, background ad loads, device-ID rotation, cookie harvesting, residential proxies, malware or adware, fake attribution and click injection. On the Content / Platform layer, examples include spoofing, dark pooling, auto reload, ad stacking, tag manipulation, sourced traffic or ad laundering and invisible ads. The list alone is not evidence; the concrete audit scope and supporting signals matter.
The framework separates Content / Platform manipulation from Identity manipulation and distinguishes directly ad-relevant mechanisms from mechanisms that are not directly advertising-specific.
Working model: categories can overlap. MFA is a supply/content/business-model pattern rather than one single fraud mechanism. The taxonomy is not exhaustive.
Explore taxonomy and methodologyH.O.T. AG charges no consulting fee for the analysis, forensic interpretation or findings session. Only FouAnalytics technical measurement costs apply and are disclosed before the audit.
See audit scope & conditions *Free refers to the H.O.T. AG consulting fee. Technical measurement costs remain chargeable.We define mandate, scope and responsibilities before the project begins – and clarify whether H.O.T. AG is needed as media consultant or independent audit function.